Determination and Display of Driving Risk

ABSTRACT

Embodiments of this invention relate to a method of determining the risk of driving a vehicle on a road network as a function of, for example, location, time of driving, weather, road conditions, driver ability, and traffic density. Historical information for the above is statistically analyzed to come up with a predictive model. Results can be displayed or presented to a driver while driving or otherwise or another person.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application63/037924 filed on 15 Aug. 2014 which is herein incorporated byreference. This application is related to PCT/IB2014/001656 publishedunder WO/2014/207558.

FIELD OF INVENTION

This invention relates to determining a risk index for driving a vehicleand displaying the risk index on a map of a transportation network oralong a proposed route of travel.

BACKGROUND

Currently navigation devices or navigation applications on a generalpurpose device have the ability to display real-time traffic informationsuperimposed on a map. In addition, individual incidents such asaccident or road construction can be displayed as icons on a map. Thiscan facilitate avoiding traffic by moving to a route that has lesstraffic or avoiding particular incidents. There is a need to perform asimilar function but to chart the overall risk of driving. Howeverdriving risk can come in a variety of forms and how to display drivingrisk is problematic. The driver's ability to respond to risky conditionsalso vary and contribute to the risk.

Real time information (while driving) is very relevant to risk. Forexample, if the road is icy, the likelihood of being in an accident ispotentially higher.

With a dynamic risk indexing system that is continually updated and alsohas real-time inputs, it is further possible to compel drivers to adjustdriving habits based on the real-time information and the driver'shistorical driving habits to reduce the risk. For example, if aparticular route is known to be icy, and the course the driver is takingis being monitored, and the monitoring system further suggests analternate non-icy route, then the driver can avoid risky conditions.Alternatively if the driver has exhibited lack of vehicle control indriving at the current speed in similar conditions, then the monitoringsystem can suggest that the driver slow down to a safe speed.

Real-time information can come from a variety of sources such aswireless acquired weather information and traffic reports. Thisinformation can further be statistically aggregated to producehistorical weather/traffic risk information likelihood indices that arespatially and temporally indexed. Metadata associated with thehistorical information can then be used to cull older information andcontinually update the indices with the latest information. Alsocontinuous, real time, accumulation of accident reports with root causescan be helpful to access and distribute that risk across the totaldriving space of some geographic region. In addition, the drivingbehavior of an individual driver when driving under specific conditionscan be monitored and factored into the risk.

Glossary

Transportation Network: A system of road, streets, paths, sidewalks,trails, waterways or other ways that a vehicle or pedestrian travelsalong. A transportation network can be subdivided by the type of vehicleor pedestrian that is intended to be used for. For example, roads andstreets may be used by cars, trucks and busses. Trails and sidewalks maybe used by pedestrians and perhaps bicycles. Transportation networks aregenerally stored in a Geographic information System that documents thelocation and interaction of various components of the transportationnetwork. Attribution is also associated with the various components ofthe network.

Element: Is a distinct component of a transportation network that has anassociated geographic coordinate/s. Examples of elements are roadsegments where the road begins and ends at an intersection; or anintersection between two or more roads.

Attribution: Attribution associated with a transportation networkincludes any piece of information that can be related to a spatiallyreferenced element or component of the transportation network. Examplesare such things as speed limits, number of lanes, connections betweencomponents, or type of vehicle that can traverse the component.Attribution, in addition to being spatially referenced may have atemporal (time) component expressed as, for example, time of day, timeof week, or time of year. An example of this is the speed limit in aschool zone.

Metadata: Metadata is a special kind of attribution associated with thequality of components of transportation network. Metadata can beassociated with individual geographic components, attribution or thesource of the geography or attribution. Metadata may be associated withprecision or accuracy of the components or source. Metadata may have acomponent that list the age of the source material or the attribute orgeometry.

Index: One or more values used to multiply or otherwise adjust up ordown a baseline value. For example, if a prospective insured basepremium is $100, discounts and/or increases to the base may be appliedby multiplying the base by a crash index, a driver age index, a safedriving index or a single index that is based an aggregate analysis of anumber of parameters.

Parameters: Any factor that may be directly or indirectly be related anindex or outcome, for example, insurance risk.

Multivariate Analysis: A class of statistical analysis used to determinethe relevance of one or more parameters in predicting an outcome andused to build a predictive function base on one or more of the analyzedparameters. In this case the outcome is the prediction of insurancerisk.

Accident Count: The number of accidents that occur for a given elementof the transportation network over a given time. This may be furthersubdivided based on weather conditions and/or time of day, time of weekor based on other attributes that may influence accident occurrence.

Incident: A single occurrence of a measured parameter. For example anindividual accident report is an incident of the parameter accidents; arecorded speed of an individual driver along a segment of road is anincident of speed of travel for that segment.

Granularity: This term is used to refer to the specificity of either anattribute or index. For example, if an accident count is based simply onthe transportation element it took place on, it is less granular than ifthe accident count is based on the location (element) and the time.

Driving Risk (or Hazard Index): This term is used collectively for allembodiments of the present invention to encompass the desired outcome ofa driving risk model. Examples of desired output is the probability of:having an accident at a given location or the probability of sustainingvehicle damage and bodily harm while driving and the anticipatedseverity of the damage or harm.

Driving Risk Attribute: Any information that may correlate statisticallyor as part of a multivariate analysis—to driving risk.

Crowd Sourced: Information that is gathered from voluntary (orotherwise) information that is contributed to a website or webservicevia an internet link. This information can be anything from verbalreports concerning traffic, to GPS tracks that observe a driverslocation and speed in real-time, which can then subsequently be used toupdate maps and other information pertaining to traffic or hazard.

Below are examples of elements of a driving risk database. Some or allof these elements may be used to develop a risk model or risk indices.

-   -   Standard GIS road network including:        -   Road Segments            -   Geography typically stored as a series of end nodes                locations, and a series of shape points (internal points                that define the location of the segment) or as a                geometric function.            -   Attributes Stored relative to a node or the segment as a                whole            -   (Road segments typical have an end node at the                intersection with another road segment or a political                boundary or a geographic feature.)        -   Intersections            -   Geography may be stored as either a singularity or a                series of point and lines which make up a complex                intersection (such as a highway cloverleaf)            -   Attributes are stored that are associated with the                intersection and/or the connecting segments        -   Maneuvers (including complex maneuvers)            -   Geography usually stored as a reference to one or more                geographic components that make up the maneuver        -   Attribution Examples (all attributes may have multiple            values base on time and may also have metadata associate            with them):            -   For Segments:                -   Speed limit/Actual Speed Driven                -   Accident Count                -   Historical Traffic Flow/Count                -   Historical Weather Information                -   Number of Lanes                -   Vehicle Type Access                -   Street Side Parking                -   Elevation/Change in Elevation        -   Railroad Crossing        -   Political Boundaries        -   Parking Areas    -   Historical Data    -   For a given transportation segment and for each accident event        on that segment        -   type of accident (solo or collision);        -   direction of travel;        -   date,        -   time of day;        -   type of vehicle;        -   weather;        -   driver record;        -   type of tires/condition;        -   cost of damage;        -   number of passengers;        -   injuries sustained;        -   road conditions;        -   traffic conditions;        -   lane closers;    -   the weather includes        -   type and amount of precipitation;        -   dewpoint;        -   wind: speed and direction;        -   smoke;        -   fog;        -   flooding;        -   temperature;        -   barometric pressure    -   For an individual driver:        -   Speed associated with a given time and location (that can            then be associated with other variables such as weather and            road conditions)        -   Risky driving behavior, for example: driving over the speed            limit; veering out of a lane, erratic speed; and sudden            braking

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention include a method to develop adatabase comprising parameters that are related to driving risk to beused for display and routing of a vehicle, where the parameters arerelated to transportation network elements and/or the individualdriver's driving characteristics.

Embodiments of the invention include determining which parameters orcombination of parameters best predicts driving risk for individualdrivers. These parameters may vary geographically for example, rural.vs. urban and due to an individual driver's historic driving behavior

Embodiments of the present invention include a maintenance and updatemethod for the above mentioned databases.

A system that comprises a database, software and hardware to predictdriving risk and display it for the driver of a vehicle is included inembodiments of this invention.

It is an object of some embodiments of this invention to display drivinghazard relative to transportation segments on a map of a transportationnetwork.

It is an object of some embodiments of this invention to display theanticipated driving risk along a route to be traveled based onhistorical data and real-time data and to depict the anticipated riskfor the anticipated time of day and/or day of week based on the relativehistorical information at specific locations along the route.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings constitute a part of this specification and includeexemplary embodiments of the invention, which may be embodied in variousforms. It is to be understood that in some instances various aspects ofthe invention may be shown exaggerated or enlarged to facilitate anunderstanding of the invention.

FIG. 1 is an embodiment of the present invention depicting a method todetermine and display driving risk.

FIG. 2 depicts an embodiment of a driving hazard map.

FIG. 3 is a flowchart of an embodiment showing how to initially developa historical driving risk database.

FIG. 4 is depicts an embodiment of how to combine risk information fromdisparate sources.

FIG. 5 is generic flowchart of multivariate analysis and modeldevelopment.

FIG. 6 is a flow chart of how a hazard index may be effected by anindividual's historical driving behavior.

DETAILED DESCRIPTION OF THE FIGURES

FIG. 1 describes a method to determine and display driving risk. Thefirst step is to compile a database 102 of factors (historicalinformation) that may be indicative of driving risk either singly orwhen used in tandem with other factors. The factors should begeo-referenced or indexed to transportation elements and optionally betemporally indexed as well. For example, attribution associated with thefactors could be time or day or time of week. Factors that may beimportant for modeling risk are described below.

Once a database is compiled, a statistical model is developed 104 suchthat given input concerning factors used in the model, a probability ofbeing in an accident can be determined.

The model can then be used with real-time information 106 acquired inthe vicinity of the location of a moving vehicle or within a defined maparea or along a proposed route. The model then generates risk indexes(probability of being in an accident) for each transportation segment ofinterest.

Finally the risk indices are displayed on a map or other graphicalindication of risk 110. The process of acquiring real-time information,calculation risk, and displaying is repeated at intervals 112, in orderto keep the risk display current with the latest risk factors.

FIG. 2 show one example of a hazard map. In the embodiment,transportation segments are depicted with superimposed indications ofhazard: one segment with a single lane 212 (hazard data that is notdistinguishable via direction of travel) and with two lanes 208. Variousforms of stippling 212 depict various levels of anticipated hazard. Asthe hazard index can change from one transportation segment to the next,a change in the index may be noted at the junction of two transportationsegments 204. In some embodiments, the present location of the vehiclein motion can be depicted on the map with some form of icon 206.Stippling or patterns 212 are just one form of representation of risk.Other forms include color coding or icons near each transportationsegment (not shown).

FIG. 3 shows one method of how to initially construct a spatiallyreferenced database, to be used to predict driving risk, based onexisting historical information. A database of historical information isneeded in order to amass hazard information based on time and location.Different information may be available for different locations. Thedevelopment of the database assumes no strong correlation between anyparameter and risk. For example there may not be a strong correlationbeing driving over the speed limit and getting in an accident, howeveras a whole, driving faster may correlate to increased damage or harm ifan accident occurs.

It is not presumed that relationships between parameters and risk holdtrue over large areas—there may be locally relevant predictors that arenot as significant as in other areas. Certain historical datasets orparameters may not be as readily available in some areas as they are inothers. For example, reports documenting accidents and accidentlocations may be more readily available and more easily input into adatabase for an urban area than for a rural area. Or accident reportsmay not be available, but traffic counts which may indicate accidentsmay be available.

Ideally the attribution used for driving risk will be easier to dealwith if it is consistent throughout the entire rating area. Toaccommodate this, it may be necessary to approximate a parameter storedin the database with input from a related parameter. For example, fromthe previous paragraph, you may wish to store accident occurrencesassociated with each road segment. If accident reports are not availablefor an area of interest but traffic flow information is, you may be ableto infer that while traffic stops or slows way down that this is causedby an accident. This could then be reflected as an accident occurrence.This inferred accident occurrence could further be reflected in themetadata as the source for the accident count and an indication that thecount is less reliable than an actual accident count. Another means ofgetting the proxy is the road quality, like road maintenance, andquality of the road surface type.

Accordingly as shown in FIG. 3, the first step 302 is to find sources ofhistorical information that potentially can be used singly or in tandemwith other parameters to predict driving risk. As pointed out above, thesources of information may vary locally, but it will be necessary tocombine or map 306 the information from different sources that representthe same parameter into a single index.

Real-time information pertinent to driving risk needs to be identified304. Real-time information could come from insurance subscribers thatopt into an insurance plan that mandates monitoring or could be crowdsourced by volunteers. Additionally real-time information could comefrom sources such as commercial traffic information providers or localgovernment highway or police departments.

Based on what historical information that is available and what quantitythere is and what type of real time information can be acquired, thedatabase schema or design can then be created 308. All parameters to bestored in the database will be geographically referenced 314 relative toan underlying GIS database 312 of the transportation network. Certainparameter (for example a speed limit) may also be temporally referenced.

Once a risk system is running based on the database, some of the data inthe database may be retired based on age or when more accurateinformation becomes available. Therefore metadata about the age andquality of the data needs to be documented 310.

FIG. 4 shows an example of how disparate information is combined into asingle layer in the risk database. The example is given for accidentreports but the technique also applies to any type of attribution. Asaccident reports initially come from local police departments and/ordirectly from insurers, the format of the information and availabilityvaries between departments or companies. For example, one departmentwill have available accident reports that are geographically referencedto a street address or an intersection 402 and another department willhave accident reports referenced to geographic coordinates 406, forexample, latitude and longitude. In an embodiment of this invention,risk attribution is referenced to components of the transportationnetwork, for example street segments or intersections, with possiblyalso direction of travel. Therefore the frame of reference of theincoming accident reports need to be translated into the frame ofreference of the database. For accident reports geographicallyreferenced to a street address or intersection 402, the reference mustbe geocoded 404 so that the segment or intersection can be associated(snapped) 408 with appropriate road segment or intersection in thedatabase. If the incoming accident report is referenced to mapcoordinates 406, then this location can simply be snapped 408 to thenearest street segment or intersection.

As is well known, the probability of an accident will increase withincreased traffic density and/or due to inclement weather. Thisinformation may be available 410 with incoming accident reports or maybe available via other sources such from a weather service which thencan be related to an accident incident via location and time.

The probably of an accident may increase based on the time. For examplethe probability of an accident most likely increases at 2 AM (2:00) onNew Years day as opposed to any other day at the same time. Thereforeany form of attribution that can be associated with an incident shouldbe added 412 so that it can be analyzed to see if there is anycorrelation with risk.

The granularity of associated information will vary. For example if atraffic flow was associated with a particular accident and that trafficflow information was acquired from a Traffic Messaging Channel (TMC),this information may not be associated with the exact location of theaccident and therefore may be suspect. The quality of the associatedattribution for accident reports needs to be documented as metadata 414.

It should be noted that initially accident reports (and otherparameters) would come from historical data such as police reports,however, this could be supplanted by real time information coming fromvehicle sensors. For example, if a system can access the output from carsensors, an accident incident could be recorded at the GPS location ofthe vehicle when there was signal indicating that the air-bag wasdeployed. Once again the source of the report or parameter should beincluded as part of the metadata and be used as a measure of quality.Other driving telemetry obtaining devices which may be installed on thevehicle would be used to obtain additional pertinent information.

Examples are shown below of incidents that can be recorded in a riskdatabase and which can subsequently be used to determine driving risk.Examples of associated attribution are also provided. These are examplesonly and is not an exhaustive list.

-   -   Accidents    -   Crime    -   Tickets    -   Vandalism    -   Insurance Payout; Fault (victim or perpetrator)    -   Road Condition (Potholes, pavement temperature, lane marking,        etc.)    -   Road Surface Type    -   Traffic Counts    -   Weather Events (Ice, Snow, Rain, Fog, Smog, Temperature)    -   Driver Distracted? Also visibility of curves, signs, traffic        lights, warning signals    -   Traffic Flow        -   Volume of Traffic        -   Speed of Traffic/Excess Speed        -   Lane Closures        -   Detours        -   Related Accidents

The following list are examples of information that may be recorded foran individual driver and may come from either/or questionnaires orreal-time sensor information: Type of car; where you drive; when youdrive; snow tires during winter; previous tickets

-   -   Real-time tracking allowed by the vehicle driver?        -   GPS, bluetooth usage (i.e. cellphone); rapid acceleration;            braking; airbag deploy; speed; other driving telemetry            devices installed in car (accelerometer, gyroscope, compass)    -   Air Bag Deployment    -   Rapid Acceleration/Deceleration    -   Swerving from lane    -   Segments and intersections traversed including time of day; time        of week; speed; braking; acceleration; lane changes; crossing        the median; bluetooth usage    -   Stopping locations; duration    -   Associated weather

Once a historical database of incidents, for example, accidents andtraffic violations is developed and referenced to transportationelements, then analysis can be performed to determine relationships torisk. Once again, no a priori assumptions are made about a correlationbetween a particular parameter and risk other than initial assumptionsthat are made to run and test a multivariate model.

In an embodiment, incidents are evaluated based on the quantity andquality of information available and also the extent over which theinformation is available. The goal is to create a risk and/or hazardindex or indices based on one or more of the type of incidents recordedrelated to elements of the transportation network.

In an embodiment, what is desired, is a function to predict thelikelihood that a driver will be involved in an accident. The likelihoodof being in an accident can be a function of:

-   -   Time    -   Location (for driving and parking)    -   Driver Performance    -   Road Conditions    -   Weather    -   Traffic Volume    -   Crime Statistics    -   Type of Vehicle    -   Number of passengers    -   Vehicle condition

These parameter can be further broken down into:

-   -   Time: time of day, time of week, time of year, holidays;        daylight/nighttime    -   Location: relative to a transportation segment, geographic        location, within a political boundary    -   Driver Performance:        -   If monitored using in-vehicle sensors while driving: amount            of distraction (mobile use); driving above or below speed            limits; weaving; rapid acceleration; road class usage; and            duration        -   From records: accident reports; speeding and other            violations    -   Road Conditions:        -   From records: potholes, sanding/salting during storms;            plowing frequency; number of police patrols; visibility            issues (like proper lighting at night, or blinding sun in            eyes)        -   From vehicle sensors: bumpiness; storm conditions; ABS            braking engaged; differential slip

The factors that may influence the risk of being in an accident may beexceedingly complex. This is why the analysis lends itself to a form ofmultivariate analysis. Typically a human can only visualize therelationship between 2, maybe 3 variables at a time and a parameter mynot be directly related to a cause of an incident, but may provide anindication of the cause. For example in one area, it may be found thatthe instance of traffic accidents at 2 AM is far greater than in anotherarea. Therefore you could conclude that time of night is not a very goodoverall predictor of having an accident. However if you also observethat in the first area, the instance of arrest for drunk and disorderlyis higher than the second area, the combination of time and arrests forintoxication, may be a much better predictor. If yet more variables areintroduced, then the relationship may get more complicated and morepoorly understood without some form of multivariate statisticalcorrelation.

In another example the quality of the information will influence thepredictive model. It is well known that ice formation on a road is afunction of temperature, humidity and barometric pressure. However ifthe weather conditions in an accident report are based on the generalweather conditions for the region from a weather report, this data willnot take into account, subtle weather variations that may be availablefrom in-car sensors. A difference of a degree in temperature could makethe difference between ice and no ice.

As shown in FIG. 5, once an initial database is constructed 502 withsome or all of the above listed information, then a predictive modelneeds to be developed. When collecting data, care must be taken to notduplicate the same incident that is recorded in multiple sources. Astatistical significance of the measurement parameters needs to beevaluated with respect to driving risk 504. For a given geographic area,it must be ascertained whether or not there is enough data to make ameaningful correlation and whether that data is of sufficient quality.If the data is of mixed quality, as in the freezing pavement exampleabove, then quality must be taken into account for the overall generalmodel. This can be done by setting a minimum threshold data qualitywhere a dataset must contain quality data for a specified percentage ofthe transportation elements within the region of interest.

It is desirable to have as much granularity in the observed informationas possible in order to determine what information correlates morestrongly to risk. Using the accident report example, we want to predictdriving risk. A model can be developed that uses part of the informationavailable as a training set 506 (for example in a neural networkpredictive model known in the art) and part of the data to test theprediction 508.

In many multivariate analysis methods, initial assumptions need to bemade to come up with a working predictive function 506. For example,initial weighting or correlation values might need to be assigned to theinput variables. An educated guess may be that the number of pot holesin a road is about half as important to risk as the number of drunkdriving arrests.

Once an initial model is generated, an iterative process 510 is used toconverge on a reasonable predictive model. This is done by modifying theweighting of input parameters slightly 512, then rerunning the newpredictive function and observing the correlation statistics until anoptimal correlation is arrived at. In embodiments, this can be doneautomatically or manually.

In an embodiment, the input for a model may need to be parameterized insuch a way as it can be used in the model. An example ofparameterization would be to characterize incidents into a grouping. Forexample, it may be desirable to collectively refer to accidents countsfalling into a range of 1-10 accidents per year as a “low” accidentcount and have “medium” and “high” counts as well.

In some embodiments, the driving risk index may be modified for anindividual driver based on their historical driving behavior relative towhere they have driven and what either the hazard index was or what thedriving conditions were like as depicted in FIG. 6. Provided a driverallows it, the driver's driving behavior is monitored 602. A database ofdriving behavior for the individual is established which compiles thedriving characteristics relative to the driving risk index and/ordriving conditions at the time of driving and the location of driving604. For example, if the driver consistently exhibits exceeding a safedriving speed during hazardous conditions, this could indicate that thedriver's likelihood of losing control and/or being in a accident isenhanced and therefore the driving risk index as displayed or used forrouting calculations should be increased 606 for this particular driver.The statistics are continually compiled, and if the driver improves overtime, this can also be observed and be incorporated in future analysis,for example, for insurance rates or individual driving risk indices.

As was previously pointed out, the parameters that could be used todriving hazard and the resulting model could be exceedingly complex.Compiling information from a variety of sources to populate a givenparameter may be difficult and if available data is insufficient, mayalso result in a poor prediction. Therefore, in order to keep the costof the risk system low and to facilitate rapid development, it may bedesirable to limit the data/parameters that are utilized and make somesimplifying assumptions.

In an embodiment, the assumption is made that risk for driving on aparticular transportation element is directly correlated to the numberof accidents reported on that element over a set time period. Thereforethe risk database could simply contain accident incidents that arerelated to individual transportation segments. If available, additionalattribution that may be recorded with accident incidents are, forexample, direction of travel, time of day, date, and weather variables.

Another embodiment comprises assembling an accident incident databaseand linking accidents incidents to transportation elements 402. If anyadditional information is available such as the time of accident, theseverity of the accident, the weather or pavement conditions, thisshould be included as associated attribution. Based on the incidentinformation, an accident count could then be developed which, in itssimplest form, would be the average number of accidents that occur oneach transportation element over a given time period. If otherattribution is available, then the accident count could be furthersubdivided based by separating data, for example, for a given time ofday or time of week, and direction of travel, thus having multipleaccident counts per transportation element. If severity information wasavailable, then accident incidents could be weighted in the accidentcount, for example, an accident with a fatality could be counted as 10times a minor accident.

A basic Hazard Index (I) (also called a driving risk index) can bedeveloped. In its simplest form, the Hazard Index is the summation ofthe accident for each transportation element for a given time period.

Yet more refinement of an individual Hazard Index could be made byfurther subdividing the index based on additional attribution such asweather and road conditions provided that the accident count databasehas this amount of granularity.

If a route is being taken, the system next looks for real-timeinformation from external sources of information—for example trafficcounts, accident reports or reports of lane closures. In addition,weather information along the route could also be acquired.

In an embodiment, while following a route, risk or hazard conditionscould be monitored in real-time and the route altered on-the-fly ifconditions change and another route is faster or safer.

System Implementation

The method/s described in this application can be implemented on aVehicle Navigation System. The vehicle navigation system comprises oneor more of:

-   -   a. A database module containing attribution associated with        transportation elements and times that is housed in a database        management system.    -   b. A driver behavior module containing attribution associated        how well an individual driver drives in relationship to driving        conditions and driving risk indices. housed in a database        management system that may or may not be separate from the        database module in item a.    -   c. A compilation module which can be implemented in software        running on a computer processor which performs statistical        analysis to determine vehicle driving risk indices based on the        attribution in the database module and from external sources of        information.    -   d. A display module, for example, which can be a display screen,        which in turn is housed on a mobile device, a computer, or a        personal navigation device or an in car navigation device or        infotainment system.    -   e. Sensors modules to acquire attribution associated with risk,        vehicles, drivers and the transportation network.    -   f. Wired or wireless communication modules functionally        connecting the various other modules.

All modules can be part of the same device or separate devices and therecan be more than one device that containing each module which work inparallel or in series.

Instructions to perform the various task can be stored on volatile ornon-volatile memory in communication with computer processor/s.

What is claimed:
 1. A method, implemented using a vehicle navigationsystem, for determining the risk associated with driving on a pluralityof elements of a transportation network by a driver comprising:compiling, in a compilation module of the navigation system, a databaseof temporal and geo-referenced driving risk attribution types for theplurality of transportation elements, for a plurality of drivers andvehicles; determining, in a risk determination module of the navigationsystem that is functionally connected to the compilation module, one ormore driving risk indices for each of the plurality of elements thatrelates at least to one of the driver risk attribution types to thelikelihood of damage to at least one of a person and property andoptionally to the probable severity of damage of the person andproperty.
 2. The method of claim 1 further comprising: displaying, in afunctionally connected display module of the navigation system, thedriving risk indices for a plurality of transportation elements for oneof: within a mapped area or, in the vicinity of a route to be traveledor being traveled or, in the vicinity of the present location of amoving vehicle.
 3. The method of claim 1 wherein the driving riskattribution types comprise at least one of the following associated witheach of a plurality of accidents: location; time; transportationelement; severity of damage and injury; weather; traffic; speed oftravel for each vehicle in each accident prior to the accident; speedlimit of the transportation segment; road condition; individual vehiclecharacteristics for each vehicle in the accident; identification of thedriver and driving record for each vehicle in the accident; and laneclosers.
 4. The method of claim 2 wherein the display of the drivingrisk indices is a color coding relative to the amount of risksuperimposed on the representation of the transportation elements. 5.The method of claim 2 wherein the indication of the driving risk indicesis one or more icons placed in proximity to the associatedtransportation element wherein at least one of the shape and color ofthe one or more icons represents relative risk.
 6. The method of claim 1wherein multiple driving risk indices are calculated: one for eachdriver risk attribute type and one for all driver risk attribute typesstatistically combined.
 7. The method of claim 6 wherein a user canselect which index to use and display.
 8. The method of claim 2 whereinthe display of driving risk flashes on and off if the risk is above aspecified threshold.
 9. The method of claim 1 wherein a message is oneor more of displayed and annunciated if all routes away from a currentor a selected location have a driving risk index greater than aspecified threshold, indicating it is unsafe to drive.
 10. The method ofclaim 2 wherein for a selected or present location of a vehicle, a safedriving speed is displayed in a corner of the map display.
 11. Themethod of claim 1 wherein the driving risk index indicates the maximumsafe driving speed for the given conditions.
 12. The method of claim 1wherein the indications of the driving risk index indicate the maximumdriving risk index given any conditions or times.
 13. The method ofclaim 2 wherein the display of driving risk indications is periodicallyupdated with real-time information.
 14. The method of claim 1 whereinonly safe route are displayed wherein a safe route has alltransportation elements that make up the route, below a minimumthreshold driving risk rating.
 15. The method of claim 1 wherein atleast in part the driving risk indices are based on the mean of thedriving ability of a plurality of drivers during specific drivingconditions and for a specific driver, the driving risk indices aremodified based on whether the driving ability of the specific driver isabove or below the mean.
 16. The method of claim 15 wherein the drivingrisk indices for the specific driver are utilized to compute aninsurance cost for driving.
 17. The method of claim 1 wherein amultivariate analysis is used to determine the driving risk indicies.18. The method of claim 1 wherein individual datum of the driving riskattribution have associated metadata indicating the age of each datumand wherein the datum are deleted after the age reaches a specificthreshold and the driving risk indices are recalculated.
 19. A vehiclenavigation system, for determining the risk associated with driving on aplurality of elements of a transportation network by a drivercomprising: a compilation module configured to capture and store in adatabase, temporal and geo-referenced driving risk attribution types forthe plurality of transportation elements, for a plurality of drivers andvehicles; a risk determination module, functionally connected to thecompilation module, configured to generate, one or more driving riskindices for each of the plurality of transportation elements thatrelates at least to one of the driver risk attribution types to thelikelihood of damage to at least one of a person and property andoptionally to the probable severity of damage of the person andproperty; and a display module, functionally connected to thecompilation and risk determination modules, configure to display thedriving risk indices for a plurality of transportation elements for oneof: within a mapped area or, in the vicinity of a route to be traveledor being traveled or, in the vicinity of the present location of amoving vehicle.
 20. The system of claim 16 wherein at least in part thedriving risk indices are based on the mean of the driving ability of aplurality of drivers during specific driving conditions and for aspecific driver, the driving risk indices are modified based on whetherthe driving ability of the specific driver is above or below the mean.